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OK, there's a billion questions:
- SQL Server
- stackoverflow.com/questions/485409/generating-a-histogram-from-column-values-in-a-database OP did not know the difference between count and histogram :-) But it's the number one Google result.
- stackoverflow.com/questions/19103991/create-range-bins-from-sql-server-table-for-histograms has a minor extra group by twist, but otherwise fine
- stackoverflow.com/questions/16268441/generate-histogram-in-sql-server
- SQLite
- stackoverflow.com/questions/67514208/how-to-optimise-creating-histogram-bins-in-sqlite perf only, benchmarking would be needed. SQLite.
- stackoverflow.com/questions/32155449/create-a-histogram-with-a-dynamic-number-of-partitions-in-sqlite variable bin size, same number of entries per bin
- stackoverflow.com/questions/60348109/histogram-for-time-periods-using-sqlite-regular-buckets-1h-wide time
- MySQL: stackoverflow.com/questions/1764881/getting-data-for-histogram-plot MySQL appears to extend
ROUNDto also round by integers:ROUND(numeric_value, -2), but this is not widely portable which is a shame - stackoverflow.com/questions/72367652/populating-empty-bins-in-a-histogram-generated-using-sql specifically asks about empty bins, which is amazing. Amazon Redshift dialect unfortunately, but answer provided works widely, and Redshift was forked from PostgreSQL, so there's hope. Those newb open source server focused projects that don't use AGPL!
Let's try it on SQLite 3.40.1, Ubuntu 23.04. Data setup:
sqlite3 tmp.sqlite 'create table t(x integer)'
sqlite3 tmp.sqlite <<EOF
insert into t values (
0,
2,
2,
3,
5,
6,
6,
8,
9,
17,
)
EOF
sqlite3 tmp.sqlite 'create index tx on t(x)'For a bin size of 5 ignoring empty ranges we can:which produces the desired:
sqlite3 tmp.sqlite <<EOF
select floor(x/5)*5 as x,
count(*) as cnt
from t
group by 1
order by 1
EOF0|4
5|5
15|1And to consider empty ranges we can use SQL which outputs the desired:
genenerate_series + as per stackoverflow.com/questions/72367652/populating-empty-bins-in-a-histogram-generated-using-sql:sqlite3 tmp.sqlite <<EOF
select x, sum(cnt) from (
select floor(x/5)*5 as x,
count(*) as cnt
from t
group by 1
union
select *, 0 as cnt from generate_series(0, 15, 5)
)
group by x
EOF0|4
5|5
10|0
15|1Basic class example.
Minimal example. Gives a hint at how boilerplate heavy Sphinx can be!
To run each example and see the output run:
./build.sh
xdg-open out/index.html Integer factorization algorithms better than Shor's algorithm by 
Ciro Santilli 40 Updated 2025-07-16
Ciro Santilli 40 Updated 2025-07-16- 2023 www.schneier.com/blog/archives/2023/01/breaking-rsa-with-a-quantum-computer.html comments on "Factoring integers with sublinear resources on a superconducting quantum processor” arxiv.org/pdf/2212.12372.pdf
A group of Chinese researchers have just published a paper claiming that they can—although they have not yet done so—break 2048-bit RSA. This is something to take seriously. It might not be correct, but it’s not obviously wrong.We have long known from Shor’s algorithm that factoring with a quantum computer is easy. But it takes a big quantum computer, on the orders of millions of qbits, to factor anything resembling the key sizes we use today. What the researchers have done is combine classical lattice reduction factoring techniques with a quantum approximate optimization algorithm. This means that they only need a quantum computer with 372 qbits, which is well within what’s possible today. (The IBM Osprey is a 433-qbit quantum computer, for example. Others are on their way as well.)
How many logical qubits are needed to run Shor's algorithm? by Ciro Santilli 40 Updated 2025-07-16
Ciro Santilli 40 Updated 2025-07-16catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch explains:
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec).
Contains several computer vision models, e.g. ResNet, all of them including pre-trained versions on some dataset, which is quite sweet.
Documentation: pytorch.org/vision/stable/index.html
Matrix multiplication example.
Fundamental since deep learning is mostly matrix multiplication.
The hello world!
Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
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Intro to OurBigBook
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This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 2. You can publish local OurBigBook lightweight markup files to either OurBigBook.com or as a static website.
Figure 3. Visual Studio Code extension installation.
Figure 5. . You can also edit articles on the Web editor without installing anything locally. Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension. 
- Infinitely deep tables of contents:
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Further documentation can be found at: docs.ourbigbook.com
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